Given an IP address, understanding its initial assignment,and predicting its potential usage can significantly help toward improving efficiency of many practical IP-based applications, such as Ad-recommendation, point of interest selection, fraud detection, and Internet service optimization. However, there are only a few prior studies conducted on predicting IP usage from its assignment. Surprisingly, there does not exist a benchmark dataset available to academia that can be used to investigate and develop IP usage predictions for different IP applications. In this work, we formulate the IP usage prediction problem; specifically, we collect large-scale, real-world data and extract salient features using sophisticated networking tools such as different network signals, trace route delay, IP block usage, and geographical landmarks. We showcase a series of tabular information retrieval methods to learn network signals and their interactions, and identify the IP usage scenarios. We believe our datasets and algorithms can benefit the community to facilitate relevant research in this domain, yielding more efficient and effective solutions to multiple categories of applications.

中文翻译：

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识别 IP 使用场景：问题、数据和基准

给定一个 IP 地址，了解其初始分配并预测其潜在用途可以显着帮助提高许多基于 IP 的实际应用程序的效率，例如广告推荐、兴趣点选择、欺诈检测和 Internet 服务优化。然而，只有少数先前的研究通过分配来预测 IP 使用情况。令人惊讶的是，学术界不存在可用于调查和开发不同 IP 应用的 IP 使用预测的基准数据集。在这项工作中，我们制定了 IP 使用预测问题；具体来说，我们使用复杂的网络工具收集大规模的真实数据并提取显着特征，例如不同的网络信号、跟踪路由延迟、IP 块使用和地理标志。我们展示了一系列表格信息检索方法来学习网络信号及其交互，并识别 IP 使用场景。我们相信我们的数据集和算法可以使社区受益，以促进该领域的相关研究，为多种应用程序提供更高效和有效的解决方案。